We’ve gone from AI defaulting to designs that look like purple slop to those that have that recognizable Claude look. To avoid this, I always create a design md file with guidelines for color, typography, and more. Here are a few places to find inspiration for your design md: 1. Go to @mobbin and install its MCP to find design patterns and app screens you like. Paste the screens and ask AI to make a design md. 2. Check out designmd sh, which has a library of design md files for popular websites and apps. 📌 I walk through an example of creating a design md in my new tutorial:

Jason is a DevEx engineer at OpenAI who wrote the official guidebook on how to use Codex for work. In tomorrow’s episode, he showed me his complete Codex work system, including how to: → Run a chief of staff across Slack and email → Turn past sessions into new skills and workflows → Build sites to learn anything, like the drums 📌 Subscribe to get the full episode tomorrow:

Anthropic recently cut Claude Code’s system prompt by 80%. @trq212 explains why: “As the models have gotten smarter, they need less direction, fewer constraints, and fewer examples. The examples are constraining it because now it’s like, ‘Oh, you want things like this example.’ If you remove the examples, it can actually be more free-form. All this is to say that you want to trim your context [when a new model is released]. The latest models often need more room to run.” 📌 Watch the full episode here:

“We’ve got /loop, /goal, and workflows. They all get the agent to run for long periods of time.” Here’s my new episode with @trq212 from the Claude Code team, where he shared how he plans, builds, and runs loops with Claude Code: → How /loop, /goal, and workflows work differently → Live demo: Editing launch videos with Claude → Using HTML artifacts to plan and learn Some quotes from Thariq: “We cut Claude Code’s system prompt by 80%. As models have gotten smarter, they need less direction, fewer constraints, and fewer examples.” “Planning is an iterative process of exploring, investigating, and finding out what you don’t know.” “One failure mode I see is that people glaze over AI’s plans. You want to make sure it’s something you read.” I've been wanting to interview Thariq for a long time and I learned so much from this epsiode. 📌 Watch now: Thanks to our sponsors: @RiversidedotFM: All-in-one AI studio for podcasts and video @WisprFlow: 4x faster than typing with your voice

Thought I’d share a fun activity I did with my 8-year-old daughter and Codex today: 1. She drew a picture of a dragon. 2. We uploaded her drawing to Codex and used image gen to make a few more dragon poses. She used voice to give Codex feedback until we got the result below. 3. We sent it to customstickers(dot)com to print sticker sheets (it's about $20 for 10 sheets). Now she can share her stickers with her friends when they arrive in the mail next week. Try it if you have kids stuck at home this summer :)

I think the days of companies relying only on frontier models are coming to an end. 1. Tokenmaxxing at frontier-model API prices makes no sense. Uber burned through its entire 2026 AI budget in 4 months, Microsoft moved engineers off Claude Code due to cost, and companies are realizing that running everything on frontier models can get expensive fast. Tokenmaxxing makes sense when you’re on a subsidized $200/month plan, but it’s unsustainable at API rates. 2. Companies will rely on a portfolio of models. Coinbase recently cut its AI spend nearly in half by switching engineers to Chinese open-source models like GLM and Kimi. Airbnb and Pinterest have done the same with Alibaba’s Qwen models. I believe this will be the default path forward: using frontier models for high-stakes work and cheaper models for everything else. 3. China’s open-source strategy is working. Chinese models are taking market share from frontier models at US companies. China is also building the full AI stack, from energy, like solar and nuclear, to data centers to domestic chips. The Chinese government is planning a $295B investment in AI data centers, with at least 80% of chips built domestically. 4. Frontier labs are in a Catch-22 situation. If they release great open-source models, they might undercut their own frontier API revenue. If they gate the best models behind a trusted list, companies will just lean on open alternatives more. The last major US open-source model was OpenAI’s gpt-oss series back in August 2025, which already feels like decades ago in the AI space. 5. The US needs to think about its AI strategy holistically. I believe restricting access to frontier models will only hurt American innovation. Banning US companies from using Chinese models won’t work either. Just look at how China took over the global electric vehicle market. To maintain our edge, we need the best closed and open models while scaling our energy grid and data centers much faster. 📌 More in my recent essay:

How I’m getting the most out of Fable before July 7: 1. Prep context with cheaper models 2. Plan with Fable, execute with another model 3. Use lower effort, like Medium, and babysit what Fable is doing 📌 Watch my full tutorial for 5 Fable-worthy use cases to try:

Thought I’d share a fun activity I did with my 8-year-old daughter and Codex today: 1. She drew a picture of a dragon. 2. We uploaded her drawing to Codex and used image gen to make a few more dragon poses. She used voice to give Codex feedback until we got the result below. 3. We sent it to customstickers(dot)com to print sticker sheets (it's about $20 for 10 sheets). Now she can share her stickers with her friends when they arrive in the mail next week. Try it if you have kids stuck at home this summer :)

I havea bunch of YouTube adsense revenue trapped in my account b/c Google won't confirm my tax info...and there's no way to reach a real human to talk to. Anyone have any connections who work at YouTube or Adsense?

I am interviewing @NousResearch Hermes co-founder @karan4d tomorrow, what topics would you like to see us cover? I already covered setup, integrations, and basic cron jobs in a previous tutorial - so what else would y'all like to hear about? This chart is a vertical line lol

I think the days of companies relying only on frontier models are coming to an end. 1. Tokenmaxxing at frontier-model API prices makes no sense. Uber burned through its entire 2026 AI budget in 4 months, Microsoft moved engineers off Claude Code due to cost, and companies are realizing that running everything on frontier models can get expensive fast. Tokenmaxxing makes sense when you’re on a subsidized $200/month plan, but it’s unsustainable at API rates. 2. Companies will rely on a portfolio of models. Coinbase recently cut its AI spend nearly in half by switching engineers to Chinese open-source models like GLM and Kimi. Airbnb and Pinterest have done the same with Alibaba’s Qwen models. I believe this will be the default path forward: using frontier models for high-stakes work and cheaper models for everything else. 3. China’s open-source strategy is working. Chinese models are taking market share from frontier models at US companies. China is also building the full AI stack, from energy, like solar and nuclear, to data centers to domestic chips. The Chinese government is planning a $295B investment in AI data centers, with at least 80% of chips built domestically. 4. Frontier labs are in a Catch-22 situation. If they release great open-source models, they might undercut their own frontier API revenue. If they gate the best models behind a trusted list, companies will just lean on open alternatives more. The last major US open-source model was OpenAI’s gpt-oss series back in August 2025, which already feels like decades ago in the AI space. 5. The US needs to think about its AI strategy holistically. I believe restricting access to frontier models will only hurt American innovation. Banning US companies from using Chinese models won’t work either. Just look at how China took over the global electric vehicle market. To maintain our edge, we need the best closed and open models while scaling our energy grid and data centers much faster. 📌 More in my recent essay:

Claude Fable 5 is finally back, but you only have until July 7 to use it on your Claude subscription. I made a new tutorial walking through 5 use cases worth trying Fable on: → Find Fable-worthy work → Get life and business advice → Make projects ship-ready → Plan the next big thing → Refactor your project or codebase As usual, it’s no BS, and I show you Fable’s actual output. 📌 Watch now:

Here's my new post with 18 hot takes on where AI is headed next: 1. The frontier-only AI stack is collapsing Companies will use a portfolio of models to save costs, with everyday tasks defaulting to low-cost open models from China. 2. The AI super app era is here Codex, Claude, Cursor, and others are competing to disrupt all knowledge work by using agents for everything you can do on a computer. 3. Traditional software risks becoming a dumb pipe for agents Agents will become the default user. They’ll use APIs and browsers to access your website and app without humans ever seeing them. 4. Cloud agents and collaboration are the next wave The future is agents living in the cloud, accessible from any device, and collaborating closely with both you and your team. This will happen very soon. 📌 Read all 18 takes now for free:

I think @charlieholtz is right - the term "software factory" implies assembly line style tasks. That can't be the future of work with AI. We should be collaborating with agents like a conductor working with an orchestra or a film director working with a crew or a chef leading a kitchen. Work needs to be creative.